The present disclosure generally relates to electrical demand forecasting for a building, and more specifically, to weather pattern based electrical demand forecasting for a building.
In general, the electrical load of a building is highly dynamic and it can oscillate within a wide range of values during the course of a day. This oscillation is caused by several factors that influence electricity demand patterns. In order to accurately forecast and control electricity demands for a building, or a cluster of buildings, all these factors and their impacts on energy consumption dynamics need to be considered. However, a complete model of all possible factors is not practically attainable due to unknown dynamical variables, lack of tools to measure their effects, and the fact that some of these variables are uncontrollable or unpredictable.
There are several known approaches for electrical demand forecasting for a building. Many of the known approaches require a large amount of historical data for training. In addition, the existing approaches can be time consuming and as such not suitable for creating a forecast in a short period of time. Current forecasting approaches use training based methods, such as regression, which require large amount of historical data. As a result, the accuracy of forecast is dependent on the availability of large amount of detailed data.